In section REF , we presented the developed method which can be used to generate solutions at any spatial query through the "Encode-Interpolate-Forecast" framework. This means that we are free to choose any architecture for the three processes. In this section we investigate the choice of the "Forecast" architecture on the predictive performance as well as zero-shot super-resolution capabilities. We compare MAgNet[CNN] and a variant that uses LSTM with attention ([1]}, [2]} on the spatial queries only. Results are shown in Table REF . We see that leveraging the interaction between the coordinates from the spatial query and those in the parent mesh enables the model to give predictions consistent to different resolutions as opposed to generating the solutions at these queries with no interaction.
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